Copyright © 2015 The Authors. Published by VGTU Press. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The material cannot be used for commercial purposes. Complex AnAlysis of finAnCiAl stAte And performAnCe of ConstruCtion enterprises Algirdas KriVKA1, Eglė STONKUTĖ2 1Faculty of Business Management, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania 2Faculty of Economics, Vilnius University, Saulėtekio al. 9, LT-10222 Vilnius, Lithuania E-mails: 1algirdas.krivka@vgtu.lt (corresponding author); 2egles.stonkutes@gmail.com Received 18 October 2015; accepted 21 November 2015 Abstract. The paper analyses the financial state and performance of large con- structions enterprises by applying financial indicators. As there is no one single decisive financial indicator enabling to objectively assess enterprise performance, the multi-criteria decision making (MCDM) methods are applied with four groups of financial ratios (profitability, liquidity, solvency and asset turnover) acting as evaluation criteria, while the alternatives assessed are two enterprises compared throughout the reference period of three years, also with the average indicator values of the whole construction sector. The weights of the criteria have been esti- mated by involving competent experts with chi-square test employed to check the degree of agreement of expert estimates. The research methodology contributes to the issue of complex evaluation of enterprise financial state and performance, while the result of the multi-criteria assessment – the ranking of enterprises and sector average with respect to financial state and performance – could be consid- ered worth attention from business owners, potential investors, customers or other possible stakeholders. Keywords: enterprise performance, financial analysis, financial ratios, profitabil- ity, liquidity, solvency, MCDM, SAW, complex analysis. JEL Classification: C44, C61, D24, G11, G30, G33, M41. 1. Introduction In the light of complicated highly competitive modern business environment, disposing comprehensive information on enterprise financial state and performance, enabling to objectively assess the position of an enterprise in the market and its competitive capa- bilities, becomes the question of vital importance. Although modern scientific research on the issue of enterprise performance evaluation propose a variety of methods based on financial and non-financial, both quantitative and qualitative criteria, it is still the pure financial approach (with the values of quantitative indicators, or financial ratios, calculated) being applied most commonly. B u s i n e s s, Ma n ag e M e n t a n d e d u c at i o n ISSN 2029-7491 / eISSN 2029-6169 2015, 13(2): 220–233 doi:10.3846/bme.2015.300 http://dx.doi.org/ 10.3846/bme.2015.300 221 Business, Management and Education, 2015, 13(2): 220–233 However, a significant drawback concerning financial ratios application has to be recognized – a comprehensive and sustainable study on enterprise financial state and performance should involve a balanced set of financial ratios, which could seem to be equally important, but very different in their nature, while there is no single financial indicator reflecting the ultimate result. Moreover, this problem becomes even more im- portant in case of comparative analysis of financial performance in a reference period or among the group of enterprises. Suppose, a part of indicators (e.g. profitability and turnover) reflect improved enterprise performance comparing to the previous year, while the others (e.g. liquidity and solvency) take a turn for the worse, thus making the results of such research quite controversial and tricky to interpret. The problem of this paper is complex quantitative evaluation of financial state and performance of construction enterprises. The aim of the research is to complexly assess the financial state and performance of construction enterprises on the basis of quantita- tive financial criteria. Facing the issue of multiple financial ratios reflecting enterprise performance, multi-criteria decision making (MCDM) methods are applied in order to calculate the value of the integrated criterion for each alternative, i.e. financial state and performance of a particular construction enterprise in a particular year. The assessment criteria are composed of the balanced set of financial ratios, with their weights being estimated by the competent experts. The structure of the paper is as follows: we start with an overview of previous re- search on enterprise performance analysis concentrating on the role of financial indica- tors and MCDM; then the methodology of the research is presented including criteria selection and their weight estimation, normalization of criteria values, and MCDM methods application; the paper is finished with the discussion of the results and conclu- sions. 2. Previous research Traditional, and probably, most popular approach to enterprise performance analysis, is based on financial results, usually being expressed with the values of financial ratios which are commonly classified into the following groups: profitability, liquidity, sol- vency, activity (turnover), and market value (Bansal 2011; Erdogan 2013; Hofmann, Lampe 2012; Kotane, Kuzmina-Merlino 2012; Mackevičius, Valkauskas 2010; Seay 2014; Zelgalve, Zaharcenko 2012). The certain advantages of the following approach in- clude the quantitative analysis, possibility to compare performance in a number of peri- ods, between different enterprises without limitations to company size (as the calculated indicators are ratios with no dimension); besides, it is always profit and market value being the ultimate results the business owners are interested in. However, there is no one single decisive indicator enabling to objectively assess the enterprise financial state and performance. Rather, enterprise financial analysis could provide quite controversial results if a part of indicators is showing good results, while this is not the case for the others; especially, taking into account possible reverse dependence between some ratios. 222 A. Krivka, E. Stonkutė. Complex analysis of financial state and performance of construction enterprises Financial indicators could also be assessed on economic sector or industry level in the empiric research based on the structure-conduct-performance (see Bain 1959), i.e. the SCP paradigm (e.g. Bhardwaj et al. 2013; Garza-Garcia 2012); moreover, the financial analysis approach in industry research (i.e. investigating industry-average val- ues of common financial ratios) gained additional importance under the conditions of the recent economic crisis: e.g. furniture industry (Li et al. 2011), logistics (Hofmann, Lampe 2012), textile (Abbas et al. 2012), agriculture (Li et al. 2011), inter-industry complex analysis (Krivka 2014). Other common approaches to enterprise performance analysis integrate financial in- dicators with other non-financial criteria. The combination of thereof is commonly sup- ported by managerial needs, as, in deed, financial criteria are considered to be lagging, clearly reflecting the past, but not saying much about the future. The balanced scorecard, or the BSC (see Kaplan, Norton 1992), offers the assessment of performance from four interrelated perspectives (financial, customer, internal business processes, learning and growth), in empiric research each represented by a set of performance evaluation criteria (Ardekani et al. 2013; Lee 2014; Panicker, Seshadri 2014; Tavana et al. 2015). Key performance indicators (KPI) also integrate financial and non-financial dimensions providing feedback from enterprise strategy implementation, as they usually represent the detail quantitative criteria enabling to assess the achievement of the objectives, laid down in company’s strategy as the desired values of the indicators, or to compare en- terprise performance with industry best or best practices using benchmarking technique (Milichovsky, Hornungová 2013; Pavláková Dočekalová et al. 2015). As it was mentioned earlier, there is no one single indicator, enabling to make the ultimate conclusion on enterprise financial state and performance, therefore modern empiric research address this problem in at least two different ways: determining the indicators having the most significant influence on enterprise financial state and perfor- mance (Bhunia, Sarkar 2011; Erdogan 2013; Hsu 2013; Pavláková Dočekalová et al. 2015) or calculating the integrated criterion characterizing enterprise performance by applying MCDM methods. The latter methods are applied involving financial indica- tors only (e.g. Ginevičius, Podviezko 2013; Hosseini et al. 2013; Krivka 2014; Liao, Ni 2014) or combining both financial and non-financial indicators in the context of the BSC (e.g. Ardekani et al. 2013; Tavana et al. 2015) or other approach, e.g. financial performance and risk analysis (Hsu 2014). Although some researchers as one of the main features, distinguishing construc- tion industry from other kinds of economic activity, indicate large scale projects with high risk of delay, growing costs and the subsequent need to manage the associated risks (González et al. 2014; Gündüz et al. 2013; Rosenfeld 2014; Zhao et al. 2013), a considerable part of construction sector studies is devoted to enterprise performance assessment, while the analysis methodology is common with those applied to other in- dustries. The paragraph below presents a brief overview of a number of recent empiric studies devoted to enterprise financial state and performance analysis in construction. 223 Business, Management and Education, 2015, 13(2): 220–233 H. Al-Malkawi and R. Pillai (2013) investigate the impact of the financial crisis on UAE real estate and construction sectors by applying liquidity, profitability, financial leverage, and turnover ratios. M. Hegazy and S. Hegazy (2012) develop a benchmark- ing model to evaluate UK construction companies’ performance by outlining the set of financial KPI (including the indicators representing liquidity, leverage, activity manage- ment, profitability, and shareholder value), together with the minimum standard values of the mentioned indicators (benchmarking technique). T. Adeleye et al. (2013) apply binary logistic regression, with financial ratios (e.g. operating expense divided by sales, cost of revenue divided by sales, long-term debt divided by total assets) coupled by non-financial indicators (e.g. age of a company, type of trade) acting as parameters, to distinguish the factors having the strongest influence on probable loss of large con- struction companies. I. E. Tsolas (2013) in the first step of his research employs data envelopment analysis (DEA) to model the performance of listed construction enterprises in two dimensions: profitability efficiency and efficiency in the market value-generating process; the second step is devoted to identifying the drivers of performance with the help of the regression model. F. Deng and H. Smyth (2013) present a comprehensive overview of studies by other researches on the topic of measuring performance of con- structions companies and distinguish 36 financial and non-financial indicators, reflecting enterprise performance, whereas the profitability indicators are considered to be used most often. In other empiric research, headed by F. Deng, factor analysis is applied in order to find out the indicators having the strongest influence on construction compa- nies’ competitiveness in China (Deng et al. 2013), and the main performance drivers for construction enterprises in the UK focusing on three groups of indicators: profit- ability measures, employee measures, and growth measures (Deng, Smyth 2014). Jin et al. (2013) apply the BSC approach for evaluating international construction compa- nies’ performance within six dimensions of indicators: financial performance, market performance, customer perspective, stakeholders, internal business processes, learning and growth. Y. S. Liu et al. (2013) investigate the relationship between market struc- ture, ownership structure and performance in Chinese construction industry under the SCP approach based on multiple regression analysis. H.-J. Kim and K. F. Reinschmidt (2012) present a study investigating market structure of US construction industry and the organizational performance of large contractors and design firms, focusing on size, growth rate, business stability, and market diversification. Y. Tan et al. (2012) analyse the impact of competition environment on performance, examining the relationship be- tween competitive strategy and performance, and indicating four main generic strategies applied by contractors classified into four groups according to their different strategic orientations: prospectors, analysers, defenders, and reactors. Tamošaitienė et al. (2011) offer a Du Point pyramid-based methodology for profitability analysis of construction projects with MCDM methods applied for estimating the best contractor. With regards to the accomplished literature review, it has to be emphasized that both financial and non-financial indicators could be applied for enterprise performance 224 A. Krivka, E. Stonkutė. Complex analysis of financial state and performance of construction enterprises analysis in the scope of different approaches; however, the decision upon relying on financial indicators only or combining them with non-financial criteria, and the choice of particular indicators depend on the aim of the research. The research presented in this paper employs financial indicators only, while such approach is based on the fol- lowing arguments: – the aim of the research considers the assessment of enterprise financial state and performance in the past reference period, with no managerial implications to on- going performance monitoring; – financial indicators are quantitative criteria, which is usually more reliable than qualitative; – MCDM methods are to be employed for evaluation, thus avoiding the problem of no single ultimate indicator to reflect enterprise performance. The following section of the paper explains the methodology of the research, includ- ing the alternatives to be assessed, the choice of financial indicators (evaluation criteria), weight estimation and an overview of MCDM methods to be applied. 3. Methods and theoretical framework The financial data of the constructions enterprises analysed in this paper is obtained from financial reports of companies listed in Nasdaq Baltic; moreover, the analysis is supplemented by the average construction industry data obtained from Statistics Lithuania (official national authority in the sphere of statistics). With regards to experi- ence of other authors (Bansal 2011; Erdogan 2013; Kotane, Kuzmina-Merlino 2012; Mackevičius, Valkauskas 2010; Seay 2014; Zelgalve, Zaharcenko 2012) the system of financial state and performance indicators is composed of four main groups of enter- prise financial ratios: profitability, liquidity, solvency and asset turnover. On the authors’ opinion, financial ratios are the most convenient way to compare financial state and performance of a specific company to its competitors and average industry data. The indicators selected for the research and their formulas are presented in Table 1. Financial state and performance of an enterprise cannot be evaluated on one fi- nancial ratio (or even group of ratios); rather, it has to be assessed from various per- spectives (different ratios and their groups). All these indicators might be contrary to each other, maximizing or minimizing, so it is necessary to find the single integrated criterion enabling to judge upon enterprise financial state and performance. In such a case MCDM methods, currently widely applied in construction (e.g. Kalibatas et al. 2012; Šaparauskas et al. 2011; Zavadskas et al. 2008), economics and management (e.g. Ginevičius et al. 2012, 2013; Ginevičius, Podviezko 2011, 2013; Hsu 2013), seem to be an appropriate tool. The research, presented in this paper, is based on financial data of calendar years 2011–2013 (since the research was conducted in 2014, the data for 2014 had not been published yet). The alternatives under evaluation are two large construction companies 225 Business, Management and Education, 2015, 13(2): 220–233 and construction industry average (see Table 2) – each of them is assessed with regards to 10 financial state and performance indicators (the scheme of evaluation is presented in Table 3). The evaluation is performed on yearly basis, so the calculations are repeated for every year from 2011 to 2013. The value ijr of the particular evaluation criterion (financial indicator) i ( 1 10= ,...,i ) for the assessed alternative (construction enterprise / industry average) j ( 1 3= ,...,j ) is calculated on the basis of officially published com- panies’ financial statements, while the industry average values of financial ratios are obtained from Statistics Lithuania. To estimate weights ωi of the financial indicators, the method of expert evaluation is applied with respect to condition 10 1 1 = ω =∑ i i . Table 2. The assessed alternatives (source: authors) No Description 1 AS “Merko Ehitus” 2 AS “Nordekon” 3 Construction industry average Table 1. Enterprise financial state and performance indicators, applied in the research (source: Erdogan 2013; Mackevičius, Valkauskas 2010; Zelgalve, Zaharcenko 2012) No Indicator Formula Profitability ratios 1 Gross margin ratio Gross profit / Sales revenues 2 Return on sales (ROS) Net profit / Sales revenues 3 Return on assets (ROA) Net profit / Total assets 4 Return on equity (ROE) Net profit / Equity Liquidity ratios 5 Current ratio Current assets / Current liabilities 6 Quick ratio (Current assets – Inventory) / Current liabilities Solvency ratios 7 Equity-to-debt ratio Equity / Total liabilities 8 Debt ratio Total liabilities / Total assets Asset turnover ratios 9 Total asset turnover Sales revenues / Average total assets 10 Accounts receivable turnover Sales revenues / Average accounts receivable 226 A. Krivka, E. Stonkutė. Complex analysis of financial state and performance of construction enterprises Table 3. The scheme of multi-criteria assessment of construction enterprises with regards to fi- nancial state and performance indicators (source: authors) Criteria Criteria values for assessed alternatives No Description Max (+) / Min (–) Weight 1 2 3 1 Gross margin ratio + w1 r1,1 r1,2 r1,3 2 Return on sales (ROS) + … … … … 3 Return on assets (ROA) + … … … … 4 Return on equity (ROE) + … … … … 5 Current ratio + w1 ri,1 ri,2 ri,3 6 Quick ratio + … … … … 7 Equity-to-debt ratio + … … … … 8 Debt ratio – … … … … 9 Total asset turnover + … … … … 10 Accounts receivable turnover + w10 r10,1 r10,2 r10,3 The result of multi-criteria evaluation is the ranking of enterprises for every year of the period from 2011 to 2013. Three MCDM methods, i.e. Sum of Ranks, Geometric Mean and SAW (Simple Additive Weighting), have been applied in the research. These methods have been chosen for several reasons: because of their popularity, simple cal- culation algorithm, which can be easily performed without a help of special software, and clear interpretation of the results obtained. Sum of Ranks calculates the sum of criteria value ranks ijr of all criteria for each j-th alternative (Ginevičius 2007): 1= = ∑ m j ij i V r , (1) where the best alternative has the lowest sum of ranks jV . Geometric Mean calculates the geometric mean of normalized criteria values ijr , and the best alternative is indicated by the highest value of the integrated criterion Π j . Initial criteria values ijr are normalized using the formula (Ginevičius et al. 2008a, 2008b, 2012; Podvezko 2011): 1 = ∑ = rij rij n rij j  , (2) and the integrated criterion values are calculated as follows: 1 = ∏Π = m m rj iji  . (3) 227 Business, Management and Education, 2015, 13(2): 220–233 SAW method calculates the sum of normalized (see formula 2) weighted values ijr of all criteria ( 1= ,...,i m ) for each j-th alternative (Ginevičius et al. 2008a, 2008b, 2012, 2013; Podvezko 2011): 1= = ω∑ m j i ij i S r , (4) while the best alternative gains the highest value of the integrated criterion jS . 4. Research procedure and results The questionnaires for estimating the weights of the selected financial state and perfor- mance indicators were submitted to a number of construction enterprises, and 7 fully filled-in forms have been received. The experts (construction enterprises’ finance depart- ment managers) were asked to evaluate the weights of the financial indicators in two steps: first to estimate the weights of the indicators inside every particular group (profit- ability, liquidity, solvency and asset turnover – see Table 1) and then the weights of four groups: the ultimate weight ωi of the i-th indicator was calculated by multiplying its weight ω gi inside the group by the weight ω g of the group in the integrated criterion: ω = ω ⋅ ωgi i g , (5) with respect to conditions: 1ω =∑ gi (for every group of indicators) and 1ω =∑ g (for the integrated criterion). Table 4 displays the ultimate weights of the evaluation criteria. Table 4. Evaluation criteria weights based on expert estimates (source: authors) Evaluation criteria Experts and criteria weights No Description 1 2 3 4 5 6 7 Average 1 Gross margin ratio 0.09 0.09 0.06 0.09 0.06 0.09 0.09 0.081 2 Return on sales (ROS) 0.09 0.15 0.15 0.09 0.12 0.06 0.15 0.116 3 Return on assets (ROA) 0.06 0.03 0.03 0.06 0.09 0.06 0.03 0.051 4 Return on equity (ROE) 0.06 0.03 0.06 0.06 0.03 0.09 0.03 0.051 5 Current ratio 0.15 0.09 0.12 0.15 0.12 0.15 0.15 0.133 6 Quick ratio 0.15 0.21 0.18 0.15 0.18 0.15 0.15 0.167 7 Equity-to-debt ratio 0.21 0.18 0.18 0.18 0.21 0.18 0.18 0.189 8 Debt ratio 0.09 0.12 0.12 0.12 0.09 0.12 0.12 0.111 9 Total asset turnover 0.06 0.06 0.04 0.06 0.06 0.06 0.04 0.054 10 Accounts receivable turnover 0.04 0.04 0.06 0.04 0.04 0.04 0.06 0.046 Totals 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 228 A. Krivka, E. Stonkutė. Complex analysis of financial state and performance of construction enterprises The agreement of experts’ responses has been tested by applying 2χ criterion with 1= −v m degrees of freedom (Ginevičius et al. 2008a, 2008b): ( ) 122 1 χ = + S rm m , (6) where m is the number of evaluation criteria, r is the number of experts, and S is the dispersion calculated by the formula: ( )2 1= = −∑ m i i S c c , (7) with ic being the sum of ranks of all r experts’ criterion i estimates, and c is the mean value of sums of all criteria ranks. Seeing that the calculated value of 2 49 53χ = . is larger than the critical value of 2 16 92χ = .cr (with the significance level of 0 05α = . and 9 degrees of freedom), the experts’ responses are considered to be in agreement. This allows us to use the average weights of the indicators in our calculations. First, the alternatives have been evaluated for every year of the reference period of 2011–2013 (Table 5). Table 5. The results of multi-criteria assessment on yearly basis (source: authors) Alternative Sum of ranks Geometric mean SAW Ultimate results Value Rank Value Rank Value Rank Sum of Ranks Ultimate Rank 2011 AS “Merko Ehitus” 19 2 0.22 3 0.33 2 7 2 AS “Nordecon” 23 3 0.23 2 0.27 3 8 3 Construction industry average 18 1 0.4 1 0.4 1 3 1 2012 AS “Merko Ehitus” 13 1 0.42 1 0.44 1 3 1 AS “Nordecon” 23 2 0.27 2 0.26 3 7 2 Construction industry average 24 3 0.26 3 0.3 2 8 3 2013 AS “Merko Ehitus” 16 1 0.37 1 0.4 1 3 1 AS “Nordecon” 21 2 0.31 2 0.28 3 7 2 Construction industry average 23 3 0.28 3 0.32 2 8 3 229 Business, Management and Education, 2015, 13(2): 220–233 The yearly results indicate that AS “Merko Ehitus” was the best alternative in 2012 and 2013, while in 2011 both enterprises performed worse than construction sector aver- age. Though all the chosen MCDM methods have provided the same result concerning the best alternative (ranked 1st), for the 2nd and 3rd ranks the results have deviated. This supports the approach of applying several MCDM methods with the ultimate ranking coming from the average results. Then the whole period of 2011–2013 was taken into account – the ultimate results of assessment of financial state and performance during the entire reference period, based on the sum of the ultimate yearly ranks, are presented in Table 6. Table 6. The final ranking of the alternatives throughout the research period of 2011–2013 (source: authors) Alternative Sum of the ultimate yearly ranks Final rank AS “Merko Ehitus” 4 1 AS “Nordekon” 7 2–3 Construction industry average 7 2–3 It has been determined that for the whole reference period the best alternative is AS “Merko Ehitus”, which means that this enterprise can be characterized by the strongest financial state and performance in 2011–2013, compared to other alternatives, whereas AS “Nordecon” performed similar to the construction sector average. 5. Conclusions The research on complex evaluation of construction enterprises’ financial state and per- formance presented in this paper is summarized by the conclusions and a glance at possible further research. Having accomplished the comprehensive literature study, it can be presumed that although modern scientific research propose a wide range of methodology for enterprise financial state and performance analysis, most of the recent studies on the topic rely on financial indicators completely or at least partially (combining them with non-financial criteria). This fact, coupled by extra arguments related to the aim of this research and reasonable preferences towards the purely quantitative approach, encouraged the au- thors of this paper to assess enterprise financial state and performance on the basis of financial indicators. Since it has been presumed in the paper that there is no single financial indicator enabling to reflect the ultimate result of enterprise financial state and performance, the balanced set of financial indicators (including profitability, liquidity, solvency and asset turnover ratios) has been applied for assessment, with the integrated criterion values being estimated with the help of MCDM methods. In the scheme of multi-criteria evalu- 230 A. Krivka, E. Stonkutė. Complex analysis of financial state and performance of construction enterprises ation financial state and performance of two enterprises along with the average construc- tion industry results in a three-year reference period are considered to be the alternatives assessed, while the criteria for evaluation are represented by the set of financial ratios with their weights being estimated by the competent experts. The results of multi-criteria assessment enable to rank the alternatives, i.e. it has been determined that taking into account the whole reference period of 2011–2013, AS “Merko Ehitus” had supreme financial state and performance results compared to their rival AS “Nordekon” and to industry average. It has to be stated though that on the basis of multi-criteria assessment it cannot be determined to what extent AS “Merko Ehitus” has outperformed the other alternatives, because MCDM methods are limited to providing rankings only. However, the results of the assessment can be considered worth attention from business owners, potential investors, customers or other possible stakeholders, as they have clearly shown that 1) AS “Merko Ehitus” is performing better than the construction industry in general; 2) AS “Merko Ehitus” is performing better than its main competitor AS “Nordekon”. 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Modelling profitability and stock market performance of listed construction firms on the Athens exchange: two-stage DEA approach, Journal of Construction Engineering and Management 139(1): 111–119. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000559 Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamošaitienė, J. 2008. Selection of the effective dwelling house walls by applying attributes values determined at intervals, Journal of Civil Engineering and Manage- ment 14(2): 85–93. http://dx.doi.org/10.3846/1392-3730.2008.14.3 Zelgalve, E.; Zaharcenko, A. 2012. Transformation of the role of financial analysis in enterprise manage- ment, Management of Organizations, Systematic Research 64: 147–167. Zhao, X.; Hwang, B.-G.; Low, S. P. 2013. Developing fuzzy enterprise risk management maturity model for construction firms, Journal of Construction Engineering and Management 139(9): 1179–1189. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000712 Algirdas KriVKA was born in 1982 in Lithuania. In 2004 he received a Bachelor of Economics, in 2006 – Master of Economics (Finance specialization), in 2010 – Doctor of Social Sciences (Econom- ics). Associate Professor at the Department of Economics and Management of Enterprises, Faculty of Business Management, Vilnius Gediminas Technical University since 2011. Research interests: market structures, industry analysis, oligopoly, competitive strategies. Eglė STONKUTĖ was born 1992 in Lithuania. In 2015 she received a Bachelor of Economics in Vil- nius Gediminas Technical University. Currently studying at Vilnius University, a Master of Accounting (Accounting and Auditing specialization). http://dx.doi.org/10.3846/1648715X.2011.586532 http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000407 http://dx.doi.org/10.1007/s10479-014-1738-8 http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000559 http://dx.doi.org/10.3846/1392-3730.2008.14.3 http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000712